Digital breast tomosynthesis (DBT) is a promising further development of two-dimensional mammography, which is the standard diagnostic method at present. Since DBT provides three-dimensional information about the breast anatomy, it reduces the number of false positives of two-dimensional mammography, which are caused by the overlaying of fibroglandular tissue. On the other hand however a diagnosis based on digital breast tomosynthesis increases the workload of a doctor making the diagnosis, since a plurality of slice recordings must be inspected.
In order to design the diagnosis, in particular the search for lesions, more efficiently it is advantageous, as well as the digital breast tomography, to continue to provide a two-dimensional mammography. Although the two-dimensional mammography could be provided by a separate recording by way of ionizing radiation, this increases the radiation stress on the tissue being examined. It is therefore advantageous to provide a synthetic two-dimensional mammography dataset based on three-dimensional digital breast tomosynthesis.
The publication by Felix Diekmann et al. “Thick Slices from Tomosynthesis Data Sets: Phantom Study for the Evaluation of Different Algorithms”, Journal of Digital Imaging 22(5), P. 519-526 (2009), discloses approaches for creating a two-dimensional mammography dataset, which sum parts of the digital breast tomosynthesis (for example in the form of a Maximal Intensity Projection MIP, or by averaging layers). Although these methods are efficient, they have problems if suspect lesions are hidden behind structures with high intensity values, or when overlapping, inconspicuous structures create false positive results in the projection.
It is known from the publication by G. van Schie et al., “Mass detection in reconstructed digital breast tomosynthesis volumes with a computer-aided detection system trained on 2D mammograms”, Medical Physics 40(4), P. 041902 (2013) that a curved two-dimensional surface can be created, which comprises the most conspicuous lesions of digital breast tomosynthesis, and that these can be represented in one plane. However distortions arise from this, so that the resulting two-dimensional image data is dissimilar to a usual two-dimensional mammography and makes a diagnosis difficult.
It is known from the publication US 20170011534 A1 that relevance values can be allocated to voxels of the digital breast tomosynthesis via a self-learning algorithm, wherein the relevance values relate to the relevance of the respective voxel for breast cancer diagnosis. Based on these relevance values, a two-dimensional image dataset is then created by weighted intensity projection. This method of operation is also problematic if suspect lesions are hidden behind structures with high intensity values or when overlapping, inconspicuous structures create false positive results in the projection.